{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "import os\n",
    "import cv2\n",
    "import h5py\n",
    "from sklearn import preprocessing\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Model, load_model, model_from_json\n",
    "from PIL import Image\n",
    "import imagehash\n",
    "import csv\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "import scipy\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = csv.reader(open('../../../../data/data_set/BIT_label.csv','r',encoding='utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('../../../../data/neural_networks/BIT_RESNET50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = []\n",
    "Y_data = []\n",
    "num_class = 6\n",
    "input_shape=(32,32,3)#3通道图像数据\n",
    "for lists in f:\n",
    "    img = cv2.imread('../../../../data/data_set/BIT/'+lists[1])\n",
    "    img = cv2.resize(img, (input_shape[0], input_shape[1]))\n",
    "    X_data.append(img)\n",
    "    Y_data.append(int(lists[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x,test_x,train_y,test_y = train_test_split(X_data,Y_data,test_size=0.4)\n",
    "train_x = np.array(train_x).astype('float32') / 255.\n",
    "train_y = np.array(train_y) \n",
    "test_x = np.array(test_x).astype('float32') / 255.\n",
    "test_y = np.array(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x_mean = np.mean(train_x, axis=0)\n",
    "test_x_mean = np.mean(test_x, axis=0)\n",
    "train_x -= train_x_mean\n",
    "test_x -= test_x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = preprocessing.LabelBinarizer().fit(np.array(range(num_class)))  # 对标签进行ont_hot编码\n",
    "train_y = lb.transform(train_y)  # 因为是多分类任务，必须进行编码处理\n",
    "test_y = lb.transform(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_array = [0, 0, 0, 0, 0, 0]\n",
    "num_array = [0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x_perdict = model(test_x)\n",
    "test_x_perdict_arg = np.argmax(test_x_perdict, axis=1)\n",
    "test_y_arg = np.argmax(test_y, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in zip(test_y_arg, test_x_perdict_arg):\n",
    "    num_array[i] = num_array[i] + 1\n",
    "    if i != j:\n",
    "        error_array[i] = error_array[i] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(error_array).sum() / np.array(num_array).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate_old = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in zip(error_array, num_array):\n",
    "    error_rate_old.append(i / j)\n",
    "error_rate_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.78      0.80       550\n",
      "           1       0.95      0.97      0.96      2313\n",
      "           2       0.77      0.70      0.73       340\n",
      "           3       0.72      0.83      0.77       184\n",
      "           4       0.98      0.91      0.94       225\n",
      "           5       0.93      0.95      0.94       328\n",
      "\n",
      "    accuracy                           0.91      3940\n",
      "   macro avg       0.86      0.86      0.86      3940\n",
      "weighted avg       0.90      0.91      0.90      3940\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(test_y_arg,test_x_perdict_arg)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算混淆矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BIT——ResNet生成的杀死率不均衡，查看原始图像和生成测试用例的混淆矩阵发现，有区别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_path = \"../../../../output/grad/ResNet50/BIT/generated_samples_2/generated_samples_d\"\n",
    "deepsmartfuzz_new_inputs = np.load(os.path.join(deepsmartfuzz_path, 'new_inputs.npy'))\n",
    "deepsmartfuzz_origin_inputs = np.load(os.path.join(deepsmartfuzz_path, 'orgin_inputs.npy'))\n",
    "deepsmartfuzz_new_outputs = np.load(os.path.join(deepsmartfuzz_path, 'new_outputs.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_x_mean = np.mean(deepsmartfuzz_new_inputs, axis=0)\n",
    "# deepsmartfuzz_new_inputs -= test_x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_perdict = model(deepsmartfuzz_new_inputs)\n",
    "deepsmartfuzz_perdict_arg = np.argmax(deepsmartfuzz_perdict, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_new_outputs_arg = np.argmax(deepsmartfuzz_new_outputs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_array = [0, 0, 0, 0, 0, 0]\n",
    "num_array = [0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in zip(deepsmartfuzz_new_outputs_arg, deepsmartfuzz_perdict_arg):\n",
    "    num_array[i] = num_array[i] + 1\n",
    "    if i != j:\n",
    "        error_array[i] = error_array[i] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6175130208333334"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(error_array).sum() / np.array(num_array).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate_new = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7690582959641256,\n",
       " 0.5236753100338218,\n",
       " 0.5277777777777778,\n",
       " 0.8970588235294118,\n",
       " 0.7471910112359551,\n",
       " 0.872]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i,j in zip(error_array, num_array):\n",
    "    error_rate_new.append(i / j)\n",
    "error_rate_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(deepsmartfuzz_new_outputs_arg,deepsmartfuzz_perdict_arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      0.23      0.20       446\n",
      "           1       0.70      0.48      0.57      1774\n",
      "           2       0.15      0.47      0.23       288\n",
      "           3       0.12      0.10      0.11       136\n",
      "           4       0.21      0.25      0.23       178\n",
      "           5       0.97      0.13      0.23       250\n",
      "\n",
      "    accuracy                           0.38      3072\n",
      "   macro avg       0.39      0.28      0.26      3072\n",
      "weighted avg       0.54      0.38      0.41      3072\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_path = \"../../../../output/grad/ResNet50/BIT/generated_samples\"\n",
    "deepsmartfuzz_new_inputs = np.load(os.path.join(deepsmartfuzz_path, 'new_inputs.npy'))\n",
    "deepsmartfuzz_origin_inputs = np.load(os.path.join(deepsmartfuzz_path, 'orgin_inputs.npy'))\n",
    "deepsmartfuzz_new_outputs = np.load(os.path.join(deepsmartfuzz_path, 'new_outputs.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_perdict = model(deepsmartfuzz_new_inputs)\n",
    "deepsmartfuzz_perdict_arg = np.argmax(deepsmartfuzz_perdict, axis=1)\n",
    "deepsmartfuzz_new_outputs_arg = np.argmax(deepsmartfuzz_new_outputs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(deepsmartfuzz_new_outputs_arg,deepsmartfuzz_perdict_arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      0.12      0.14       447\n",
      "           1       0.74      0.62      0.68      1758\n",
      "           2       0.15      0.48      0.22       266\n",
      "           3       0.27      0.32      0.29       128\n",
      "           4       0.37      0.33      0.35       208\n",
      "           5       0.97      0.26      0.41       265\n",
      "\n",
      "    accuracy                           0.47      3072\n",
      "   macro avg       0.44      0.35      0.35      3072\n",
      "weighted avg       0.58      0.47      0.50      3072\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'error_rate_old' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-5e1bf67e7da4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mold\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_rate_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_rate_old\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0merror_rate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0merror_rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'error_rate_old' is not defined"
     ]
    }
   ],
   "source": [
    "for new,old in zip(error_rate_new, error_rate_old):\n",
    "    error_rate.append(new - old)\n",
    "error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_rate_old = [0.82, 0.95, 0.77, 0.72, 0.98, 0.93]\n",
    "precision_rate_new = [0.17, 0.70, 0.15, 0.12, 0.21, 0.93]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6499999999999999, 0.25, 0.62, 0.6, 0.77, 0.0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(precision_rate_old, precision_rate_new):\n",
    "    precision_rate.append(old - new)\n",
    "precision_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_rate_old = [0.78, 0.97, 0.70, 0.83, 0.91, 0.95]\n",
    "recall_rate_new = [0.23, 0.48, 0.47, 0.10, 0.25, 0.13]\n",
    "recall_rate_new_r = [0.12, 0.62, 0.48, 0.32, 0.33, 0.26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.66, 0.35, 0.21999999999999997, 0.51, 0.5800000000000001, 0.69]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(recall_rate_old, recall_rate_new_r):\n",
    "    recall_rate.append(old - new)\n",
    "recall_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_rate_old = [0.80, 0.96, 0.73, 0.77, 0.94, 0.94]\n",
    "f1_rate_new = [0.20, 0.57, 0.23, 0.11, 0.23, 0.23]\n",
    "f1_rate_new_r = [0.14, 0.68, 0.22, 0.29, 0.35, 0.41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6000000000000001, 0.39, 0.5, 0.66, 0.71, 0.71]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(f1_rate_old, f1_rate_new):\n",
    "    f1_rate.append(old - new)\n",
    "f1_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画杀死率图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kill_per_class = [68,59,44,56,58,58]\n",
    "kill_per_class_2 = [68,59,44,56,58,58]\n",
    "kill_origin = [26,28,15,28,30,27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42, 31, 29, 28, 28, 31]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_rate = []\n",
    "for old,new in zip(kill_per_class, kill_origin):\n",
    "    kill_rate.append(old - new)\n",
    "kill_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kill_origin = np.array(kill_origin)\n",
    "kill_per_class = np.array(kill_per_class)\n",
    "kill_per_class_2 = np.array(kill_per_class_2)\n",
    "kill_rate = np.array(kill_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_per_index = np.arange(1, 7)\n",
    "kill_per_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SUV', '轿车', '微型客车', '小型货车', '公共汽车', '卡车']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 公共汽车、微型客车、小型货车、轿车、SUV和卡车。每种车型的车辆数量分别为558辆、883辆、476辆、5922辆、1392辆和822辆。\n",
    "class_name = ['SUV', 'Sedan', 'Microbus', 'Minivan', 'Bus', 'Truck']\n",
    "[1392, 5922, 883, 476, 558, 822]\n",
    "['SUV','轿车','微型客车','小型货车','公共汽车','卡车']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_rate)\n",
    "# plt.plot(data01[\"month\"],data01[\"VALUE\"], c=\"red\",label=\"1948\")\n",
    "# plt.plot(data02[\"month\"],data02[\"VALUE\"], c=\"blue\",label=\"1949\")\n",
    "# plt.plot(data03[\"month\"],data03[\"VALUE\"], c=\"green\",label=\"1950\")\n",
    "# plt.plot(x_axix, train_pn_dis, color='skyblue', label='PN distance')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 75) # y轴范围\n",
    "# plt.title('simple-0.47')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_per_class_2)\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 75) # y轴范围\n",
    "# plt.title('simple-0.92')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class_2')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_rate/75, c=\"red\",label=\"kill_rate\")\n",
    "plt.plot(kill_per_index, recall_rate, c=\"blue\",label=\"recall_rate\")\n",
    "plt.plot(kill_per_index, f1_rate, c=\"green\",label=\"f1_rate\")\n",
    "plt.plot(kill_per_index, precision_rate, c='peru', label='precision_rate')\n",
    "# plt.plot(kill_per_index, recall_rate, c='lime', label='recall_rate')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 1) # y轴范围\n",
    "plt.title('BIT-ResNet50')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_origin/75, c=\"lime\",label=\"kill_rate_origin\")\n",
    "plt.plot(kill_per_index, kill_rate/75, c=\"red\",label=\"kill_rate_add\")\n",
    "plt.plot(kill_per_index, kill_per_class/75, c=\"peru\",label=\"kill_rate_new\")\n",
    "plt.plot(kill_per_index, f1_rate_old, c=\"blue\",label=\"f1_origin\")\n",
    "# plt.plot(kill_per_index, recall_rate, c='lime', label='recall_rate')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 1) # y轴范围\n",
    "plt.title('BIT-ResNet50')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5882352941176471, 0.21941787095461004)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate_old,kill_origin/75)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5507824838698261, 0.2573693316225744)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate_old,kill_origin/75)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6377481392176932, 0.17307111024125976)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate_old,kill_origin/75)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结束的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15401027590842434, 0.7708110837169226)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49280538030458115, 0.3206325828658273)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1449427589131121, 0.7841083696021083)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "过程中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.35824886041591925, 0.4856159410605649)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.26482044885142486, 0.6120552385657049)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1765469659009499, 0.7379309324353432)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6000000000000001, 0.39, 0.5, 0.66, 0.71, 0.71]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f1 in recall_rate:\n",
    "    total = total + math.pow((f1 - np.array(recall_rate).mean()), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1690833333333334"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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